
Novelty of the book: Novelty of a book is a key for its acceptability/success in the readers' circle. For each book in Goodreads, a short summary about the book is provided. We separately crawl this `About' information of the book (say, the document containing the summary of the book be \(A\) ) and of all the other books that are published before this book in question (say, the concatenated summary of all those books be \(B\) ). We then extract keywordshttps://github.com/aneesha/RAKE [1]} from documents \(A\)  and \(B\)  respectively (\(Keywords_A\)  and \(Keywords_B\) ) and design a metric of keyword overlap as follows:
\(\small Overlap(A, B) = \frac{|Keywords_A \cap Keywords_B |}{min (|Keywords_A|, |Keywords_B|)}\) 
Higher the keyword overlap, lower is the novelty score. We use this as a feature for our model.
We also define another novelty feature which is measured as the Kullback-Leibler (KL) divergence between the unigram language model inferred from the document \(A\)  containing all the words except the stop words from the book's summary for the \(i^{th}\)  book and the background language model from document \(B\)  and formally defined this as follows:
\(KLDiv (i)= -\sum _{w \in A}p(w|A)\times \log \frac{p(w|A)}{p(w|B)}\) 
Higher the divergence value, higher is the novelty of the book. Once again this is used a feature in the prediction model.

Genres - As mentioned earlier, genres of a book is an important feature in deciding the destiny of it. In this prediction model, we consider top 15 genres for Amazon best sellers and other books as shown in table REF . In total we have 24 genres - fantasy, fiction, nonfiction, children, romance etc. We use each of them as a binary feature.

